Hands-on solution for real-time stock market monitoring and forecasting
FinRobot offers a complete solution through its "Market Forecasting Agent" functional module and four-tier architecture:
- Configure the base environment:
- Creating a Python 3.10 virtual environment and installing FinRobot dependencies
- Configure the OpenAI API key in OAI_CONFIG_LIST
- Add financial data API licenses such as Finnhub to config_api_keys
- Core implementation steps:
- Calling the Market_Analyst agent (see text for sample code)
- Set human_input_mode to "NEVER" for fully automatic operation.
- Pass in the analysis instructions containing the target company's code via the chat method
- Key technical points:
- Integrate multiple sources of information such as market data and news using a perception layer
- Specialized domain analysis through the financial LLM algorithmic layer
- Generating decision recommendations using a financial chain thinking (CoT) approach
Typical application scenario: When forecasting the price movement of a specific stock such as NVDA for the next 7 days, the system will: 1) collect real-time trading data 2) analyze relevant financial news 3) generate a technical analysis report containing support/resistance levels.
This answer comes from the articleFinRobot: An Intelligent Body to Improve Financial Data Analysis Efficiency and Investment ResearchThe
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